Apparatus for controlling and optimizing the production of chemical substances are well known in the prior art. Reaction parameters effecting the quantity and quality of the product generated include concentration levels of each reactant, temperature conditions, flow rates, and residence times. Varying one or more of the reaction parameters generally results in a change in product yield. It is therefore advantageous to optimize such reaction parameters to maximize production and quality.
A basic prior art optimization procedure is as follows. Initial reaction conditions from an initial synthesis are used as a starting point. Using temperature, reaction time and concentrations at the values determined in the initial synthesis, three experiments with different equivalents (i.e. different stoichiometric ratios) are conducted. For example, where the initial synthesis was based on using a 1:1 ratio of a first reactant and a second reactant, ratios such as 1:1.1, 1:1.2, 1.1.3, 1.1:1, 1.2:1, and 1.3:1 could be employed. In a second set of experiments, three different temperature conditions are applied. A third and fourth set of three experiments each are also performed, changing other variables in each set. After twelve such experiments have been performed (i.e., four sets of three experiments), the results are reviewed, and optimized reaction parameters are defined, based on the data collected from the twelve experiments. An additional set of twelve experiments can then be performed, similarly varying the optimized parameters defined by the first series of experiments. In such an optimization procedure, typically twenty four experiments are required for a first optimization of reagent equivalents, temperature conditions, reaction time, and reagent concentration for a given reaction, as each experiment is repeated to check the reproducibility of the results. One disadvantage of this approach is that interactions between these parameters are difficult to quantify.
Because of this difficulty, process optimization methods referred to as statistical design experiments have been developed. The goal of such methods is to model an equation in order to couple process variables with process results (i.e., the yield of a reaction). A well known two-value approach requires 2n+1 experiments, where n is the number of variables. Each variable is employed at two different values, and an additional experiment is performed using the mean of each variable (as a control to determine if the behavior is linear). Typically, every experiment is repeated to estimate the reproducibility. For the above-mentioned case, (24+1)*2=34 experiments are needed. The disadvantage of such an empirical approach is the fact that the success of the optimization is largely based on how well each of the two values for each variable is selected. Selecting levels that are close together results in only small improvements in optimization being achieved; so that it is likely an additional 2n+1 sets of experiments will be required. Selecting values that are far apart results in a risk that one or more variables will exceed a critical parameter, which will significantly affect yields (such as exceeding a reaction temperature beyond which yield drops sharply or no reaction takes place). When this result occurs, the initial set of 2n+1 experiments are of little value, and the experiments must be repeated after different values have been selected.
Furthermore, if the mean value experiment indicates that non linear behavior exists, then it is necessary to determine the impact of quadratic terms. This step can only be assessed by expanding the design of experiments to 3n experiments, where the three values are defined as the lower and upper bounds, as well as the mean values of these bounds. For the analysis of a four-parameter system, this approach implies a total of 34=81 experiments will be required. Preferably, each set is repeated to validate reproducibility, so that a total of 162 experiments must be performed. In practice, some terms and factors in an equation model are often identical, and it is not unusual for the 81 experiments noted above to be reduced to about 40 experiments (without the duplication for validation of reproducibility).
This analysis can be performed efficiently using software packages that determine the values for each experiment, the order in which these values should be changed, and evaluate the outcome to provide a mathematical relationship between the performance criteria being investigated and the variables to be adjusted to optimize the performance. Today, equipment for parallel batch experiments is also available, so that a number of experiments can be conducted at the same time. These parallel analysis systems are based on matrices of reaction modules in which the chemicals to be analyzed are input manually at variable concentrations. Some reaction conditions, such as temperature, are often identical for all the vessels being analyzed at any given time due to the physical dimensions and limitations of the system. The reaction duration is also generally identical for efficient analysis. Due to the discrete nature of experimentation, the evaluation at the end of the experiment has to be performed for all reaction modules separately, to determine the performance of each system. These results are analyzed off-line as one data set for a fixed temperature and reaction duration. Experimentation at different reaction temperatures requires the generation of another matrix with the same reactants, and repetition of the experiments at the new temperature, as well as a new analysis of the collected data. Once the analysis for concentrations and concentration ratios at different temperatures is completed, the same set of experiments can be performed to determine the effect of reaction time on yield. The repetitiveness of such experiments (i.e., the batch-like processing) is enforced due to the matrix-like structure of the parallel reaction vessels.
While such methods can enable optimized reaction parameters to be achieved, it would be desirable to provide a method and apparatus based on optimizing reactions parameters using a continuously running system, as opposed to using the batch-based testing of the prior art.